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help > RE: regression as anova
Sep 30, 2015 05:09 PM | Alfonso Nieto-Castanon - Boston University
RE: regression as anova
Dear Liron,
The p-value that you get for that contrast is what you describe as a "model-level result" that combines all of the individual tests -each represented by a single line of this contrast- into a single p-value. To be more precise, the p-value represents the false-positive level of a F-test looking at any between-group or emotion-related differences in connectivity. For example, a p-value of .01 there means that the probability of observing your data (observed connecivity values) assuming that there were no differences in connectivity between the groups as well as no emotion-related differences in connectivity between your subjects is just 1%. Typically if you get a p<.05 value you would reject this null hypothesis (concluding that there are either between-group differences in connectivity and/or emotion-related differences in connectivity in your data). After you reach this conclusion, you can perform post-hoc tests to evaluate whether this result was due to the presence of differences in connectivity between your groups, emotion-related differences between your subjects, or both.
Hope this helps
Alfonso
Originally posted by L R:
The p-value that you get for that contrast is what you describe as a "model-level result" that combines all of the individual tests -each represented by a single line of this contrast- into a single p-value. To be more precise, the p-value represents the false-positive level of a F-test looking at any between-group or emotion-related differences in connectivity. For example, a p-value of .01 there means that the probability of observing your data (observed connecivity values) assuming that there were no differences in connectivity between the groups as well as no emotion-related differences in connectivity between your subjects is just 1%. Typically if you get a p<.05 value you would reject this null hypothesis (concluding that there are either between-group differences in connectivity and/or emotion-related differences in connectivity in your data). After you reach this conclusion, you can perform post-hoc tests to evaluate whether this result was due to the presence of differences in connectivity between your groups, emotion-related differences between your subjects, or both.
Hope this helps
Alfonso
Originally posted by L R:
Dear Alfonso
Sorry for going back to this question (and thanks so much for always having the patients to answer!), I just want to make sure I'm 100% clear on the meaning of the results. When I use the 3-line contrast you suggested
[1 -1 0 0 0 0 0 0;
0 1 -1 0 0 0 0 0;
0 0 0 1 0 0 0 0]
The p value that I get is for the one most significant comparison (i.e if the first and third lines both have significant result I get the one that is stronger)? Or is it a model-level result that takes all lines into one combined value?
Thanks!!!
Liron.
Sorry for going back to this question (and thanks so much for always having the patients to answer!), I just want to make sure I'm 100% clear on the meaning of the results. When I use the 3-line contrast you suggested
[1 -1 0 0 0 0 0 0;
0 1 -1 0 0 0 0 0;
0 0 0 1 0 0 0 0]
The p value that I get is for the one most significant comparison (i.e if the first and third lines both have significant result I get the one that is stronger)? Or is it a model-level result that takes all lines into one combined value?
Thanks!!!
Liron.
Threaded View
| Title | Author | Date |
|---|---|---|
| L R | Jul 8, 2015 | |
| Alfonso Nieto-Castanon | Jul 9, 2015 | |
| L R | Jul 10, 2015 | |
| Alfonso Nieto-Castanon | Jul 16, 2015 | |
| L R | Sep 30, 2015 | |
| Alfonso Nieto-Castanon | Sep 30, 2015 | |
| Alfonso Nieto-Castanon | Sep 30, 2015 | |
| L R | Jul 21, 2015 | |
| Alfonso Nieto-Castanon | Jul 24, 2015 | |
| L R | Jul 24, 2015 | |